The New Stack encourages its readers to contribute to Towards Data Science, a leading platform for data science and AI. Recognizing the increasing convergence of cloud infrastructure, DevOps, and AI engineering, the article invites practitioners to share their experiences with building and deploying AI systems. Successful TDS submissions are technically detailed, timely, and specific. Authors can also benefit from editorial support, promotion, and potential payment opportunities, while building their reputation within the AI community.
The article details “autoresearch,” a project by Karpathy where an AI agent autonomously experiments with training a small language model (nanochat) to improve its performance. The agent modifies the `train.py` file, trains for a fixed 5-minute period, and evaluates the results, repeating this process to iteratively refine the model. The project aims to demonstrate autonomous AI research, focusing on a simplified, single-GPU setup with a clear metric (validation bits per byte).
* **Autonomous Research:** The core concept of AI-driven experimentation.
* **nanochat:** The small language model used for training.
* **Fixed Time Budget:** Each experiment runs for exactly 5 minutes.
* **program.md:** The file containing instructions for the AI agent.
* **Single-File Modification:** The agent only edits `train.py`.
* **Autonomous Research:** The core concept of AI-driven experimentation.
* **nanochat:** The small language model used for training.
* **Fixed Time Budget:** Each experiment runs for exactly 5 minutes.
* **program.md:** The file containing instructions for the AI agent.
* **Single-File Modification:** The agent only edits `train.py`.
This article details how to use Ollama to run large language models locally, protecting sensitive data by keeping it on your machine. It covers installation, usage with Python, LangChain, and LangGraph, and provides a practical example with FinanceGPT, while also discussing the tradeoffs of using local LLMs.
yoagent is a simple, effective agent loop with tool execution and event streaming in Rust. Inspired by pi-agent-core. It features a stateful agent, multi-provider support, built-in tools, and context management.
Google is announcing the public preview of the Developer Knowledge API and its associated Model Context Protocol (MCP) server. These tools provide a machine-readable gateway to Google’s official developer documentation, enabling AI assistants to access accurate and up-to-date information for building with Google technologies like Firebase, Android, and Google Cloud.
This article discusses how to effectively prompt local Large Language Models (LLMs) like those run with LM Studio or Ollama. It explains that local LLMs behave differently than cloud-based models and require more explicit and structured prompts for optimal results. The article provides guidance on how to craft better prompts, including using clear language, breaking down tasks into steps, and providing examples.
An exploration of Claude 3 Opus's coding capabilities, specifically its ability to generate a functional CLI tool for the Minimax algorithm with a single prompt. The article details the prompt used, the generated code, and the successful execution of the tool, highlighting Claude's impressive one-shot learning and code generation abilities.
FastCode is a token-efficient framework for comprehensive code understanding and analysis, delivering superior speed, exceptional accuracy, and cost-effectiveness for large-scale codebases and software architectures. It features a three-phase framework for semantic-structural code representation, lightning-fast codebase navigation, and cost-efficient context management.
The RTX 3090 offers a compelling combination of performance and 24GB of VRAM, making it a better choice for local LLM and AI workloads than newer Nvidia Blackwell GPUs like the RTX 5070 and even the RTX 5080, due to VRAM limitations and pricing.
An analysis of the current LLM landscape in 2026, focusing on the shift from 'vibe coding' to more efficient and controlled workflows for software development and data analysis. The author advocates for tools like AI Studio and OpenCode, and discusses the strengths of models like Gemini 2.5 Pro and Claude Sonnet.
